The Logic of Sociological Inquiry In this course students will be introduced to the basic tools of sociological research including problem formulation, strategies of design and data collection, and analysis and presentation of results. This class will help students formulate a research question and develop a research strategy to best explore that question. Those strategies may include interviews, structured observation, participant observation, content analysis, and surveys. This class, strongly recommended for juniors, will culminate in the submission of a senior project proposal. (SOAN 0105 or SOCI 0105)

Inequality and the American Dream In this course we will explore sociological attempts to explain who “gets ahead” in the contemporary United States. We will discuss two distinct issues that are often conflated in public discussions: economic inequality and social mobility.  We will consider the conceptual and empirical associations between these measures, how each has changed over time, how the United States compares to other countries, and how different social environments (such as colleges, neighborhoods, and families) influence life chances within and across generations. We will also examine the challenges of producing research about these topics, focusing on both theoretical and methodological issues.

Higher Education and Society Concerns about quality, value, and cost have raised doubts about whether higher education remains a pathway to opportunity. In this course we will consider these issues by reviewing research on the changing demographics of students, the evolving definition of “merit” in admissions, the challenges of assessing what students learn, and the relationship between student loan debt and economic inequality. We will also examine how college shapes later outcomes such as income, health, and family formation. Finally, we will discuss efforts to reform higher education and the potential for innovations like global expansion to reshape postsecondary schooling in the future. 3 hrs.lct.

Social Statistics In this course we will learn the practical tools social sociologists and other scientists use to analyze data quantitatively. Topics will emphasize applications with statistical software and data from the General Social Survey and other datasets. We will explore methods to describe statistics about samples, apply the principles of probability to make predictions about populations, and estimate the significance of those predictions through inference and hypothesis testing. We will conclude with an introduction to linear regression. (Open only to majors or by Instructor Approval)

Topics in Sociological Practice In this project-based course, we will use tools of data analysis and digital methods to explore sociological research topics and communicate findings. We will review examples of public social science such as the Opportunity Atlas, Social Explorer, and the Open Policing Project. Through readings and workshops, we will reflect on how these forms create challenges and opportunities related to data ethics, principles of data visualization, and digital pedagogy. Students will build on their previous coursework to develop interactive websites that can be integrated throughout the department's curricula. Students will extensively use programs such as R and Datawrapper but no previous experience with these tools is required. (Open to Sociology majors)

The Family in Contemporary Society This course will investigate the social, economic, and political forces that have brought about changes in family life in the beginning of the 21st century. We will begin by looking at various attempts to define "the family," and we will then explore a range of topics, including the webs of family relationships (e.g., mothering, fathering, kin networks), labor and family intersections (e.g., mediating between work and family; the household division of labor), gay and lesbian family life, and domestic violence. Although the focus will be on contemporary United States, we will also examine some cross-cultural and historical material.

Data Science Across Disciplines In this course, we will gain exposure to the entire data science pipeline—obtaining and cleaning, large and messy data sets, exploring these data and creating engaging visualizations, and communicating insights from the data in a meaningful manner. During morning sessions, we will learn the tools and techniques required to explore new and exciting data sets. During afternoon sessions, students will work in small groups with one of several faculty members on domain-specific research projects in Biology, Geography, History, Mathematics/Statistics and Sociology. This course will use the R programming language. No prior experience with R is necessary.  BIOL 1230: Students enrolled in Professor Casey’s (Biology) afternoon section will use the tools of data science to investigate the drivers of tick abundance and tick-borne disease risk. To do this students will draw from a nation-wide ecological database.  GEOG 1230: In this section, we will investigate human vulnerability to natural hazards in the United States using location-based text data about hurricane and flood disasters from social media. We will analyze data qualitatively, temporally, and spatially to gain insights into the human experience of previous disasters and disaster response. We will present findings using spatial data visualizations with the aim of informing future disaster preparedness and resilience.  HIST 1230: In U.S. history, racial differences and discrimination have powerfully shaped who benefited from land and farm ownership. How can historians use data to understand the history of race and farming? Students will wrangle county- and state-level data from the U.S. Census of Agriculture from 1840-1912 to create visualizations and apps that allow us to find patterns in the history of race and land, to discover new questions we might not know to ask, and to create tools to better reveal connections between race, land, and farming for a general audience.    STAT 1230: In this course students will dive into the world of data science by focusing on invasive species monitoring data. Early detection is crucial to controlling many invasive species; however, there is a knowledge gap regarding the sampling effort needed to detect the invader early. In this course, we will work with decades of invasive species monitoring data collected across the United States to better understand how environmental variables play a role in the sampling effort required to detect invasive species. Students will gain experience in the entire data science pipeline, but the primary focus will be on data scraping, data visualization, and communication of data-based results to scientists and policymakers.  SOCI 1230:  Do sports fans care about climate change? Can sports communication be used to engage audiences on environmental sustainability? In this section of the course, students will use the tools of data science to examine whether interest in sports is associated with climate change knowledge, attitudes and behaviors, as well as other political opinions. Participants will use survey data to produce visualizations and exploratory analyses about the relationship between sports fandom and attitudes about environmental sustainability.

Matt Lawrence

Reviews and ratings for Matt Lawrence at Middlebury College.Matt Lawrence teaches Soc Research Methods, Inequality & American Dream, Higher Education and Society and more.  Explore top rated instructors and find the best for you.Is Matt Lawrence good ? Find out on MiddCourses.

I quite enjoyed this class, and no class has ever gotten me closer to considering a sociology major. This served not only as a good introductory course, but a specific one, in that you got to understand many of the base topics within sociology while looking at them through a frame you wanted to learn about. Loved prof. Lawrence.

Prof. Lawrence chooses the readings very well, many of them cut deep into the American society. Even if you are not interested in sociology, this course would still interest you. Not a lot of homework, but there would be a paper of 12-15 pages at the end of the semester.

I have taken Profesor Lawrence a few times and he truly is one of my favorite professors at Middlebury. He is extremely fair in his grading is easy and open to talk to (meaning if you don't do well on an assignment he will make sure you are equipped with a good understanding on how to improve making it very difficult to do poorly more than once). He is a very engaging lecturer which I believe is in part to how much discussion goes on in his class. he rarely talks for more than 15 minutes in a row without some form of discussion involving the entire class. This being said it is important to attend class as participation in discussions is vital. There is a lot of reading but it's more than ok to skim it as long as you have an overall understanding to help equip you with something to talk about in the class.